At Bose Corporation, we believe sound is the most powerful force on earth — and for over 60 years, we have been a company built on innovation, excellence, and independence. Privately owned, fiercely customer-focused, and driven by our values, we continue to lead industries and transform lives through sound. 

Today, Bose Corporation is entering an exciting new era. Across multiple global Business Units and Global Functions, we are shaping the future of audio technology, automotive, luxury, and premium experiences. We invite you to join us in this transformation. 

Job Description

ABOUT BOSE CORPORATION

At Bose Corporation, we believe sound is the most powerful force on earth — and for over 60 years, we have been a company built on innovation, excellence, and independence. Privately owned, fiercely customer-focused, and driven by our values, we continue to lead industries and transform lives through sound. 

Today, Bose Corporation is entering an exciting new era. Across multiple global Business Units and Global Functions, we are shaping the future of audio technology, automotive, luxury, and premium experiences. We invite you to join us in this transformation. 

ABOUT THE CO-OP PROGRAM

Bose Corporation is seeking students to participate in our Co-op Program who share our belief that sound is power. During the six-month co-op, students will have the chance to apply classroom learning in a hands-on work environment. Co-op participants will network across the business to gain diverse perspectives within Bose Corporation. Additionally, co-ops will connect with other students and colleagues, building valuable professional relationships for the future.

Fall Co-op: Candidates must be available to work full-time (40 hours per week) in a hybrid, in-office format from July 12 through December 17, 2027. No relocation assistance is available.

Being currently authorized to work in the U.S. is a precondition of employment for this position.

THE ROLE

We're looking for a Co-op to join our research team and explore how AI agents can support engineers working on active sound management technologies.

The role sits at the boundary between signal processing and software engineering. You don't need to be an active noise control expert on day one; you will learn the domain from senior engineers who build these tools. You should be a strong programmer who is comfortable reading engineering code and asking why it works.

In this role, you will:

• Capture expert knowledge. Work with senior engineers to document engineering workflows: key decisions, heuristics, failure modes, and what a finished result looks like.
• Modernize engineering tools. Refactor existing tools into modular functions with well-defined inputs and outputs.
• Connect tools to AI agents. Make engineering functions available to LLM-based agents using Python and modern tool-calling interfaces.
• Measure what works. Build an evaluation framework that compares agent results against expert-produced references.
• Build in safeguards. Implement automated checks that keep agent outputs within engineering and safety limits.
• Prototype and demonstrate. Build an agent-assisted workflow and demonstrate it on a real engineering use case.
• Report and document. Present your results and document your work so the team can build on it after your term.

• To excel in this role, candidates should possess the following:
• Currently pursuing a PhD or MS in Electrical Engineering, Computer Engineering, Computer Science, Mechanical Engineering, or a related field
• Strong Python: writing modular code, packaging functions, using git, and writing tests
• Working knowledge of MATLAB, enough to read and refactor existing engineering scripts
• Signal processing fundamentals: FFT, spectra, transfer functions, coherence, and digital filters
• Hands-on experience calling an LLM API or building a simple agent or tool-using app, in a project, course, or internship
• Clear written communication; the role involves documenting expert knowledge, not just code

Preferred Qualifications:

• Coursework or projects in adaptive filtering, active noise control, or acoustics
• Background in optimal filtering, multichannel systems, or control systems
• Familiarity with Simulink
• Experience with LLM agent frameworks, tool calling, or agent protocols such as MCP
• Experience designing evaluations or test suites for ML or LLM systems
• Experience integrating MATLAB and Python
• Comfort with large measurement datasets (time series, .mat or HDF5 files) and with plotting results clearly

WHAT YOU WILL LEARN

• How active noise cancellation systems are measured, designed, and optimized
• How to turn expert engineering judgment into reliable, testable software
• How to build and evaluate an LLM agent for a real engineering task where reliability matters

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